CN117197150A - Method and system for controlling stability of monorail crane based on artificial intelligence - Google Patents
Method and system for controlling stability of monorail crane based on artificial intelligence Download PDFInfo
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Abstract
The invention relates to the field of stability control of an overhead monorail, in particular to an artificial intelligence-based stability control method and system of the overhead monorail, which comprises the following steps: acquiring real-time images of the monorail crane during operation, and acquiring a characteristic image of the steel wire rope by utilizing a semantic segmentation network; determining an oscillation point of the steel wire rope according to the characteristic image of the steel wire rope; calculating a primary stability coefficient of the monorail crane based on the oscillation point; collecting a standard image set of the monorail crane; calculating a correction coefficient based on the real-time image and the standard image set to obtain an effective stability coefficient; and (5) comparing the effective stability coefficient with a preset threshold value to adjust the retraction rope speed of the monorail crane. According to the invention, the primary stability coefficient is calculated through the oscillation point of the steel wire rope, and then the correction coefficient is used for correcting the primary stability coefficient to obtain the effective stability coefficient, and the rope collecting speed and the rope releasing speed of the monorail crane are regulated according to the effective stability coefficient, so that the safety of the monorail crane operation is ensured.
Description
Technical Field
The present invention relates generally to the field of stability control of monorail cranes. More particularly, the invention relates to an artificial intelligence-based method and system for controlling stability of an overhead monorail.
Background
The monorail hoist refers to a transport device that runs on a suspended monorail and is composed of a tractor (for wire rope traction), a brake car, a carrier car, etc. The stability of the monorail car has an important role for the safe use of the monorail car, and thus the control of the stability of the monorail car is not negligible. Through effective control measures, the balance of the monorail crane can be ensured when the heavy object is hung and moved, and the overturning and accidents are prevented. Stability control not only protects the safety of operators, but also ensures the integrity of equipment and surrounding environment, and improves the operation efficiency and quality.
The working environment of the monorail crane is outdoor operation, and mainly comprises a port, a wharf and a factory, the working environment based on the monorail crane is influenced by wind force easily in the actual operation process, the wind force can cause the load carrying vehicle and the hoisted goods to deviate, the stability of the monorail crane is influenced, and the rope reeling speed and the rope unreeling speed of the steel wire rope are required to be reduced under the condition of high wind force so that the monorail crane can stably operate, therefore, the detection and adjustment of the rope reeling speed and the rope unreeling speed of the steel wire rope to the stability of the monorail crane are of great significance for the safe operation of the monorail crane.
Disclosure of Invention
To solve one or more of the above-mentioned technical problems, the present invention proposes to obtain an effective stability factor of an overhead monorail by calculating a preliminary stability to the overhead monorail and obtaining an effective stability factor of the overhead monorail according to the correction factor, and to adjust a rope reeling speed of the overhead monorail according to the effective stability factor, and to provide the solution in various aspects as follows.
The stability control method of the monorail crane based on the artificial intelligence comprises the following steps:
acquiring a real-time image during operation of the monorail crane, and processing the real-time image by utilizing a semantic segmentation network to obtain a characteristic image of the steel wire rope;
calculating a first swing dividing point and a second swing dividing point of the wire rope characteristic in the wire rope characteristic image to determine an oscillation point of the wire rope;
calculating a preliminary stability coefficient of the monorail crane based on the oscillation point;
acquiring standard operation images of the monorail crane under the condition of no load and no wind so as to obtain a standard image set, and calculating a correction coefficient based on the real-time images and the standard image set;
correcting the preliminary stability coefficient based on the correction coefficient to obtain an effective stability coefficient;
and comparing the effective stability coefficient with a preset threshold value to adjust the rope reeling and unreeling speed of the monorail crane.
In one embodiment, the wire rope features in the wire rope feature image are segmented according to a segmentation function to determine a first swing segmentation point and a second swing segmentation point of the wire rope, wherein the segmentation function has the expression:
in the method, in the process of the invention,is the maximum inter-class variance of the first swing point in the characteristic image of the steel wire rope, ++>The maximum inter-class difference of the second swing point in the steel wire rope characteristic image is represented by y, which is a set of horizontal position coordinates of the pixel point in the steel wire rope characteristic image, < >>Is the mean value of the horizontal position coordinates of the pixel points in the first classification of the horizontal extension of the steel wire rope in the characteristic image of the steel wire rope, and is +.>Is the mean value of the coordinate set of the pixel points in the second classification which extends horizontally in the characteristic image of the steel wire rope, +.>Is the average value of all coordinate sets of all points of the horizontal extension of the steel wire rope in the characteristic image of the steel wire rope, and is>Is the ratio of the sum of the abscissas in the first classification point of the horizontal extension of the wire rope in the wire rope characteristic image to the sum of the abscissas of all points, +.>The ratio of the sum of the abscissas in the second classification points of the horizontal extension of the steel wire rope in the steel wire rope characteristic image to the sum of the abscissas of all the points;
for the extreme difference of the pre-classification of the horizontal extension of the wire rope in the wire rope characteristic image, +.>Is the very poor of the post-classification of the horizontal extension of the wire rope in the wire rope characteristic image, +.>Is the whole extremely poor and +.>For the ratio of the sum of the abscissas of all points to the sum of the abscissas of all points in the pre-classification of the horizontal extension of the wire rope, +.>Is the ratio of the sum of the abscissas of all points to the sum of the abscissas of all points in the post-classification in which the wire rope extends horizontally.
In one embodiment, the maximum value of all maximum inter-class variance data is taken as a first marking point; taking the maximum value in all maximum inter-class polar difference data as a second mark point; and taking the middle point of the first mark point and the second mark point as an oscillation point.
In one embodiment, the primary stability factor is the ratio of the length from the point of oscillation to the point of initiation of the unwinding and winding of the wire rope to the length of the total elongation of the wire rope.
In one embodiment, the correction coefficient is calculated by:
acquiring standard operation images of a wire rope from a maximum extension position to a minimum extension position of the monorail crane under the condition of no load and no wind to construct a standard image set;
carrying out target detection on the bearing vehicle in the standard image set to obtain a minimum external rectangular frame of the bearing vehicle, and calculating the area S of the bearing vehicle according to the minimum external rectangular frame;
recording the area S of the standard image concentrated bearing vehicle corresponding to the actual length L of the steel wire rope, and making a relation diagram of the area of the bearing vehicle corresponding to the length of the steel wire rope;
calculating the area S of the bearing vehicle in the real-time image 1 Recording the actual length L of the corresponding steel wire rope 1 ；
Searching the actual length L of the steel wire rope 1 Corresponding vehicle area S in the diagram 2 In S form 2 And S is equal to 1 As a correction factor.
In one embodiment, the product of the correction coefficient and the preliminary stabilization coefficient is taken as the effective stabilization coefficient.
An artificial intelligence based overhead monorail stability control system comprising: a processor; and a memory in which computer instructions of an artificial intelligence based method of controlling stability of an overhead monorail are stored, which when executed by the processor, perform the artificial intelligence based method of controlling stability of an overhead monorail.
The invention has the beneficial effects that: the vibration point of the steel wire rope is obtained by calculating the first swing dividing point and the second swing dividing point of the steel wire rope, the primary stability coefficient is calculated based on the vibration point, then the correction coefficient is utilized to correct the primary stability coefficient to obtain the effective stability coefficient, and the rope collecting speed and the rope releasing speed of the monorail crane are adjusted according to the effective stability coefficient, so that the safety of the monorail crane operation is ensured.
Drawings
The above, as well as additional purposes, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description when read in conjunction with the accompanying drawings. In the drawings, embodiments of the invention are illustrated by way of example and not by way of limitation, and like reference numerals refer to similar or corresponding parts and in which:
fig. 1 is a flow diagram schematically illustrating an embodiment of the present invention.
Fig. 2 is a schematic view of an overhead monorail schematically showing an embodiment of the invention.
Fig. 3 is a schematic diagram of a steel cord schematically illustrating an embodiment of the present invention.
Fig. 4 is a graph schematically illustrating the relationship of wire rope length versus vehicle area for an embodiment of the present invention.
Fig. 5 is a system architecture diagram schematically illustrating an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.
Referring to fig. 1 to 4, the stability control method of the monorail hoist based on artificial intelligence comprises the following steps:
acquiring a real-time image during operation of the monorail crane, and processing the real-time image by utilizing a semantic segmentation network to obtain a steel wire rope characteristic image;
the specific method is as follows:
shooting an image of the monorail crane by placing cameras in the front-rear direction of the monorail crane, and obtaining a plurality of images of the monorail crane as a training set;
performing semantic segmentation processing on the monorail crane image by using a semantic segmentation network, and marking the area of the steel wire rope 10;
training a single-rail crane image with a label of a region of the steel wire rope 10 in a semantic segmentation network, and marking RGB three-channel values of the region of the steel wire rope 10 as followsOther areas are marked +.>Responding to the loss function smaller than a preset value, completing training of the semantic segmentation network, and obtaining a traction steel wire rope segmentation network;
and (3) acquiring real-time images of the monorail crane during operation, and throwing the monorail crane into a steel wire rope segmentation network to obtain a steel wire rope characteristic image.
Calculating a first swing dividing point and a second swing dividing point of the wire rope characteristic in the wire rope characteristic image to determine an oscillation point of the wire rope;
when the vehicle 20 is subjected to wind, the entire wire rope 10 swings. In this embodiment, the wire rope features in the wire rope feature image are segmented according to a segmentation function to determine a first swing segmentation point and a second swing segmentation point of the wire rope, where the expression of the segmentation function is:
in the method, in the process of the invention,is the maximum inter-class variance of the first swing point in the characteristic image of the steel wire rope, ++>The maximum inter-class difference of the second swing point in the steel wire rope characteristic image is represented by y, which is a set of horizontal position coordinates of the pixel point in the steel wire rope characteristic image, < >>Is the mean value of the horizontal position coordinates of the pixel points in the first classification of the horizontal extension of the steel wire rope in the characteristic image of the steel wire rope, and is +.>Is the mean value of the coordinate set of the pixel points in the second classification which extends horizontally in the characteristic image of the steel wire rope, +.>Is the average value of all coordinate sets of all points of the horizontal extension of the steel wire rope in the characteristic image of the steel wire rope, and is>Is the ratio of the sum of the abscissas in the first classification point of the horizontal extension of the wire rope in the wire rope characteristic image to the sum of the abscissas of all points, +.>Is the ratio of the sum of the abscissas in the second classification points of the horizontal extension of the wire rope in the wire rope characteristic image to the sum of the abscissas of all points +.>For the extreme difference of the pre-classification of the horizontal extension of the wire rope in the wire rope characteristic image, +.>Is the very poor of the post-classification of the horizontal extension of the wire rope in the wire rope characteristic image, +.>Is the whole extremely poor and +.>For the ratio of the sum of the abscissas of all points to the sum of the abscissas of all points in the pre-classification of the horizontal extension of the wire rope, +.>Is a steel wireThe ratio of the sum of the abscissas of all points to the sum of the abscissas of all points in the total in the post-classification in which the string extends horizontally.
In the formula (1), classifying coordinates of the wire rope pixel points in the horizontal direction in the wire rope characteristic image by variance, wherein the first classification is fluctuation of the wire rope pixel points in the horizontal direction in the image, and a fluctuation range section is (a) 1 ，b 1 ) The second classification is that the fluctuation of the steel wire rope pixel points in the image in the horizontal direction is that the fluctuation range interval is (b) 1 ，c 1 ）。
In the formula (2), the coordinates of the wire rope pixel points in the horizontal direction in the wire rope characteristic image are classified by the extreme difference, and the coordinate swing amplitude interval of the wire rope pixel points in the horizontal direction in the previous classification is (a) 2 ，b 2 ) The coordinate swing amplitude interval of the steel wire rope pixel point in the horizontal direction in the image is (b) 2 ，c 2 ）。
Taking the maximum value in all maximum inter-class variance data as a first marking point; taking the maximum value in all maximum inter-class polar difference data as a second mark point; and taking the middle point of the first mark point and the second mark point as an oscillation point. As shown in fig. 3, the first marking point is located above the second marking point, in fig. 3, the first marking point, the oscillating point and the second marking point are sequentially located from top to bottom, the first marking point divides the steel wire rope 10 into a first section and a second section according to the first section, the fluctuation amplitude of each point in the first section is similar, the fluctuation amplitude of each point in the second section is similar, and the fluctuation amplitude of each point in the first section is greatly different from the fluctuation amplitude of each point in the second section. Similarly, the second marking point divides the steel wire rope 10 into a first section and a second section according to the formula (2), the swing amplitude of each point in the first section is similar, the swing amplitude of each point in the second section is similar, and the fluctuation amplitude of each point in the first section is greatly different from the fluctuation amplitude of each point in the second section.
The primary stability factor of the monorail car is calculated based on the oscillation point, in this embodiment the stability factor is the ratio of the length between the position of the oscillation point of the wire rope 10 to the position of the start point of reeling and unreeling the wire rope 10 to the length of the total elongation of the wire rope 10.
Acquiring standard operation images of the monorail crane under the no-load and no-wind conditions to obtain a standard image set; the carrier 20 swings under the action of wind force, the wire rope 10 is obviously deflected at the oscillation point, in other words, the wire rope 10 is approximately vertical above the oscillation point, and the wire rope 10 deflects to be arc-shaped under the oscillation point, so as to extend in the horizontal direction. The further the oscillation point is from the vehicle 20, the less stable the condition of the vehicle 20, and the lower the stability factor of the monorail car.
The correction coefficient is calculated based on the real-time image and the standard image set, and in this embodiment, the calculation mode of the correction coefficient is as follows:
acquiring standard operation images of the wire rope 10 from the maximum extension to the minimum extension of the monorail crane under the no-load and no-wind conditions to construct a standard image set;
performing target detection on the carrier 20 in each image in the standard image set to obtain a minimum circumscribed rectangular frame of the carrier 20, and calculating the area S of the carrier 20 according to the minimum circumscribed rectangular frame;
the actual length L of the wire rope 10 is recorded to correspond to the area S of the carriage 20 in the standard operation image, and a relation diagram of the actual wire rope 10 length L and the area S of the carriage 20 is made, and the relation diagram is shown in fig. 4.
For example: when the actual elongation length of the wire rope 10 is 5 meters, the area of the carriage 20 is s1 in the standard operation image; when the length of the wire rope 10 actually extended is 4 meters, the area of the carriage 20 is s2 in the standard operation image; when the length of the wire rope 10 actually extended is 3 meters, the area of the carriage 20 is s3 in the standard operation image; when the actual elongation of the wire rope 10 is 2 meters, the area of the carriage 20 is s4 in the standard work image.
Calculating the area S of the vehicle in the real-time image 1 Recording the actual length L of the corresponding steel wire rope 1 ；
Searching the actual length L of the steel wire rope 1 Corresponding vehicle area S in the diagram 2 In S form 2 And S is equal to 1 As a correction systemA number.
For example, when the length of the wire rope 10 actually extended under the working condition of the monorail hoist is 5 meters, the correction coefficient is a1/s1 when the area of the carriage 20 is a1 in the first image set, and the oscillation correction coefficient k is smaller than 1 because the area of the carriage 20 is maximum when it is not deviated in the vertical photographing direction, which means that the oscillation component on the vertical photographing surface is larger at this time as the coefficient is smaller.
In the above embodiment, during the operation of the monorail crane, the cargo is affected by wind force, so that the carrier 20 and the cargo swing, and since the swing of the carrier 20 in the vertical shooting direction is not considered during the calculation of the preliminary stability factor, the actual swing condition of the carrier 20 cannot be accurately expressed, and thus the correction factor is introduced to correct the preliminary stability factor.
In one embodiment, the product of the correction coefficient and the preliminary stabilization coefficient is used as the effective stabilization coefficient.
And comparing the effective stability coefficient with a preset threshold value to adjust the retracting rope speed of the monorail crane. And setting an effective stability coefficient threshold value a of the monorail crane, and slowing down the rope reeling and unreeling speed until the effective stability coefficient of the monorail crane is larger than the threshold value when the effective stability coefficient of the monorail crane in an image during the monorail crane operation is smaller than the threshold value. And the speed is used as the rope winding and unwinding speed of the monorail crane. Illustratively, a is 0.9.
In the above steps, the oscillating point of the wire rope 10 is obtained by calculating the first oscillating dividing point and the second oscillating dividing point of the wire rope 10, the primary stability coefficient is calculated based on the oscillating point, then the primary stability coefficient is corrected by using the correction coefficient to obtain the effective stability coefficient, and the rope collecting speed and the rope releasing speed of the monorail crane are adjusted according to the effective stability coefficient, so that the safety of the monorail crane operation is ensured.
Fig. 5 is a schematic frame diagram of an artificial intelligence based stability control system for an monorail hoist according to an embodiment of the invention. The apparatus 40 comprises a processor and a memory storing computer program instructions which, when executed by the processor, implement the artificial intelligence based method for controlling the stability of an monorail hoist according to the first aspect of the invention. The device also includes other components, such as communication buses and communication interfaces, known to those skilled in the art, whose arrangement and function are known in the art and therefore will not be described in detail herein.
In the context of this patent, the foregoing memory may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, the computer readable storage medium may be any suitable magnetic or magneto-optical storage medium, such as, for example, resistance change Memory RRAM (Resistive Random Access Memory), dynamic Random Access Memory DRAM (Dynamic Random Access Memory), static Random Access Memory SRAM (Static Random-Access Memory), enhanced dynamic Random Access Memory EDRAM (Enhanced Dynamic Random Access Memory), high-Bandwidth Memory HBM (High-Bandwidth Memory), hybrid storage cube HMC (Hybrid Memory Cube), etc., or any other medium that may be used to store the desired information and that may be accessed by an application, a module, or both. Any such computer storage media may be part of, or accessible by, or connectable to, the device. Any of the applications or modules described herein may be implemented using computer-readable/executable instructions that may be stored or otherwise maintained by such computer-readable media.
In the description of the present specification, the meaning of "a plurality", "a number" or "a plurality" is at least two, for example, two, three or more, etc., unless explicitly defined otherwise.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the invention, which are described in detail and are not to be construed as limiting the scope of the claims. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (7)
1. The stability control method of the monorail crane based on the artificial intelligence is characterized by comprising the following steps of:
acquiring a real-time image during operation of the monorail crane, and processing the real-time image by utilizing a semantic segmentation network to obtain a characteristic image of the steel wire rope;
calculating a first swing dividing point and a second swing dividing point of the wire rope characteristic in the wire rope characteristic image to determine an oscillation point of the wire rope;
calculating a preliminary stability coefficient of the monorail crane based on the oscillation point;
acquiring standard operation images of the monorail crane under the condition of no load and no wind so as to obtain a standard image set, and calculating a correction coefficient based on the real-time images and the standard image set;
correcting the preliminary stability coefficient based on the correction coefficient to obtain an effective stability coefficient;
and comparing the effective stability coefficient with a preset threshold value to adjust the rope reeling and unreeling speed of the monorail crane.
2. The method for controlling stability of an artificial intelligence-based monorail hoist according to claim 1, wherein the wire rope features in the wire rope feature image are segmented according to a segmentation function to determine a first swing segmentation point and a second swing segmentation point of the wire rope, and the segmentation function has an expression:
in the method, in the process of the invention,characterised by the wire ropeMaximum inter-class variance of first swing point in image, +.>The maximum inter-class difference of the second swing point in the steel wire rope characteristic image is represented by y, which is a set of horizontal position coordinates of the pixel point in the steel wire rope characteristic image, < >>Is the mean value of the horizontal position coordinates of the pixel points in the first classification of the horizontal extension of the steel wire rope in the characteristic image of the steel wire rope, and is +.>Is the mean value of the coordinate set of the pixel points in the second classification which extends horizontally in the characteristic image of the steel wire rope, +.>Is the average value of all coordinate sets of all points of the horizontal extension of the steel wire rope in the characteristic image of the steel wire rope, and is>Is the ratio of the sum of the abscissas in the first classification point of the horizontal extension of the wire rope in the wire rope characteristic image to the sum of the abscissas of all points, +.>The ratio of the sum of the abscissas in the second classification points of the horizontal extension of the steel wire rope in the steel wire rope characteristic image to the sum of the abscissas of all the points; />For the extreme difference of the pre-classification of the horizontal extension of the wire rope in the wire rope characteristic image, +.>Is the very poor of the post-classification of the horizontal extension of the wire rope in the wire rope characteristic image, +.>Is the whole extremely poor and +.>For the ratio of the sum of the abscissas of all points to the sum of the abscissas of all points in the pre-classification of the horizontal extension of the wire rope, +.>Is the ratio of the sum of the abscissas of all points to the sum of the abscissas of all points in the post-classification in which the wire rope extends horizontally.
3. The method for controlling stability of an artificial intelligence-based monorail hoist according to claim 2, characterized in that a maximum value among all the maximum inter-class variance data is taken as a first marker point; taking the maximum value in all the maximum inter-class range data as a second mark point; and taking the middle point of the first mark point and the second mark point as an oscillation point.
4. The method for controlling stability of an artificial intelligence-based monorail hoist according to claim 1, wherein the preliminary stability factor is a ratio of a length from an oscillation point to a start point of a wire rope reeling and unreeling to a length of a total elongation of the wire rope.
5. The method for controlling stability of an artificial intelligence-based monorail hoist according to claim 1, wherein the correction coefficient is calculated by:
acquiring standard operation images of a wire rope from a maximum extension position to a minimum extension position of the monorail crane under the condition of no load and no wind to construct a standard image set;
carrying out target detection on the bearing vehicle in the standard image set to obtain a minimum external rectangular frame of the bearing vehicle, and calculating the area S of the bearing vehicle according to the minimum external rectangular frame;
recording the area S of the standard image concentrated bearing vehicle corresponding to the actual length L of the steel wire rope, and making a relation diagram of the area of the bearing vehicle corresponding to the length of the steel wire rope;
calculating the area S of the bearing vehicle in the real-time image 1 Recording the actual length L of the corresponding steel wire rope 1 ；
Searching the actual length L of the steel wire rope 1 Corresponding vehicle area S in the diagram 2 In S form 2 And S is equal to 1 As a correction factor.
6. The method for controlling stability of an artificial intelligence based monorail hoist according to claim 1, characterized in that the product of the correction coefficient and the preliminary stability coefficient is taken as an effective stability coefficient.
7. Monorail hoist stability control system based on artificial intelligence, its characterized in that includes:
a processor; and
a memory having stored therein computer instructions of an artificial intelligence based method of controlling the stability of an overhead monorail, which, when executed by the processor, performs the artificial intelligence based method of controlling the stability of an overhead monorail of any one of claims 1-6.
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